Update README.md

README.md

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 # ParaDetox: Detoxification with Parallel Data (English)
 
-This repository contains information about Paradetox dataset -- the first parallel corpus for the detoxification task -- as well as models and evaluation methodology for the detoxification of English texts. The original paper ["ParaDetox: Detoxification with Parallel Data"](https://aclanthology.org/2022.acl-long.469/) was presented at ACL 2022 main conference.
+This repository contains information about ParaDetox dataset -- the first parallel corpus for the detoxification task -- as well as models and evaluation methodology for the detoxification of English texts. The original paper ["ParaDetox: Detoxification with Parallel Data"](https://aclanthology.org/2022.acl-long.469/) was presented at ACL 2022 main conference.
 
 ## ParaDetox Collection Pipeline
 
@@ -31,16 +31,16 @@ In addition to all ParaDetox dataset, we also make public [samples](https://hugg
 # Detoxification evaluation
 
 The automatic evaluation of the model were produced based on three parameters:
-* *style transfer accuracy* (**STA**): percentage of nontoxic outputs identified by a style classifier. We pretrained toxicity classifier on Jigsaw data and put it online in HuggingFace🤗 [repo](https://huggingface.co/SkolkovoInstitute/roberta_toxicity_classifier).
+* *style transfer accuracy* (**STA**): percentage of nontoxic outputs identified by a style classifier. We pretrained toxicity classifier on Jigsaw data and put it online in HuggingFace🤗 [repo](https://huggingface.co/s-nlp/roberta_toxicity_classifier).
 * *content preservation* (**SIM**): cosine similarity between the embeddings of the original text and the output computed with the model of [Wieting et al. (2019)](https://aclanthology.org/P19-1427/).
-* *fluency* (**FL**): percentage of fluent sentences identified by a RoBERTa-based classifier of linguistic acceptability trained on the [CoLA dataset](https://nyu-mll.github.io/CoLA/). 
+* *fluency* (**FL**): percentage of fluent sentences identified by a RoBERTa-based classifier of linguistic acceptability trained on the [CoLA dataset](https://nyu-mll.github.io/CoLA/).
 
 All code used for our experiments to evluate different detoxifcation models can be run via Colab notebook [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1xTqbx7IPF8bVL2bDCfQSDarA43mIPefE?usp=sharing)
 
 ## Detoxification model
-**New SOTA** for detoxification task -- BART (base) model trained on ParaDetox dataset -- we released online in HuggingFace🤗 repository [here](https://huggingface.co/SkolkovoInstitute/bart-base-detox).
+At-the-time SOTA for text detoxification task in English -- BART (base) model trained on ParaDetox dataset -- we release online in HuggingFace🤗 [repo](https://huggingface.co/s-nlp/bart-base-detox).
 
-You can also check out our [demo](https://detoxifier.nlp.zhores.net/junction/) and telegram [bot](https://t.me/rudetoxifierbot).
+You can also check out our [web-demo](https://detoxifier.nlp.zhores.net/junction/).
 
 ## Citation
 
@@ -68,7 +68,7 @@ You can also check out our [demo](https://detoxifier.nlp.zhores.net/junction/) a
 
 ## Contacts
 
-If you find some issue, do not hesitate to add it to [Github Issues](https://github.com/skoltech-nlp/paradetox/issues).
+If you find some issue, do not hesitate to add it to [Github Issues](https://github.com/s-nlp/paradetox/issues).
 
 For any questions and get the TEST SET, please, contact: Daryna Dementieva (dardem96@gmail.com), Daniil Moskovskiy (Daniil.Moskovskiy@skoltech.ru), or Alexander Panchenko (a.panchenko@skol.tech)